Overview

Dataset statistics

Number of variables23
Number of observations145460
Missing cells343248
Missing cells (%)10.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.5 MiB
Average record size in memory184.0 B

Variable types

Categorical5
Numeric16
Boolean2

Alerts

Date has a high cardinality: 3436 distinct values High cardinality
MinTemp is highly correlated with MaxTemp and 3 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 3 other fieldsHigh correlation
Evaporation is highly correlated with MinTemp and 4 other fieldsHigh correlation
Sunshine is highly correlated with Humidity9am and 4 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeedHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeedHigh correlation
Humidity9am is highly correlated with Evaporation and 2 other fieldsHigh correlation
Humidity3pm is highly correlated with Sunshine and 4 other fieldsHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with Sunshine and 2 other fieldsHigh correlation
Cloud3pm is highly correlated with Sunshine and 2 other fieldsHigh correlation
Temp9am is highly correlated with MinTemp and 3 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 5 other fieldsHigh correlation
MinTemp is highly correlated with MaxTemp and 2 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 5 other fieldsHigh correlation
Evaporation is highly correlated with MaxTemp and 3 other fieldsHigh correlation
Sunshine is highly correlated with Humidity3pm and 2 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
Humidity9am is highly correlated with MaxTemp and 2 other fieldsHigh correlation
Humidity3pm is highly correlated with MaxTemp and 5 other fieldsHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with Sunshine and 2 other fieldsHigh correlation
Cloud3pm is highly correlated with Sunshine and 2 other fieldsHigh correlation
Temp9am is highly correlated with MinTemp and 3 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 4 other fieldsHigh correlation
MinTemp is highly correlated with MaxTemp and 2 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 3 other fieldsHigh correlation
Evaporation is highly correlated with MaxTempHigh correlation
Sunshine is highly correlated with Cloud9am and 1 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed3pmHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeedHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with SunshineHigh correlation
Cloud3pm is highly correlated with SunshineHigh correlation
Temp9am is highly correlated with MinTemp and 2 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 2 other fieldsHigh correlation
Location is highly correlated with MinTemp and 8 other fieldsHigh correlation
MinTemp is highly correlated with Location and 5 other fieldsHigh correlation
MaxTemp is highly correlated with Location and 5 other fieldsHigh correlation
Sunshine is highly correlated with Humidity9am and 4 other fieldsHigh correlation
WindGustDir is highly correlated with Location and 2 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindDir9am is highly correlated with Location and 2 other fieldsHigh correlation
WindDir3pm is highly correlated with Location and 2 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
Humidity9am is highly correlated with Location and 6 other fieldsHigh correlation
Humidity3pm is highly correlated with Location and 8 other fieldsHigh correlation
Pressure9am is highly correlated with MinTemp and 2 other fieldsHigh correlation
Pressure3pm is highly correlated with MinTemp and 2 other fieldsHigh correlation
Cloud9am is highly correlated with Sunshine and 3 other fieldsHigh correlation
Cloud3pm is highly correlated with Sunshine and 3 other fieldsHigh correlation
Temp9am is highly correlated with Location and 6 other fieldsHigh correlation
Temp3pm is highly correlated with Location and 5 other fieldsHigh correlation
RainToday is highly correlated with Humidity3pmHigh correlation
RainTomorrow is highly correlated with Sunshine and 2 other fieldsHigh correlation
MinTemp has 1485 (1.0%) missing values Missing
Rainfall has 3261 (2.2%) missing values Missing
Evaporation has 62790 (43.2%) missing values Missing
Sunshine has 69835 (48.0%) missing values Missing
WindGustDir has 10326 (7.1%) missing values Missing
WindGustSpeed has 10263 (7.1%) missing values Missing
WindDir9am has 10566 (7.3%) missing values Missing
WindDir3pm has 4228 (2.9%) missing values Missing
WindSpeed9am has 1767 (1.2%) missing values Missing
WindSpeed3pm has 3062 (2.1%) missing values Missing
Humidity9am has 2654 (1.8%) missing values Missing
Humidity3pm has 4507 (3.1%) missing values Missing
Pressure9am has 15065 (10.4%) missing values Missing
Pressure3pm has 15028 (10.3%) missing values Missing
Cloud9am has 55888 (38.4%) missing values Missing
Cloud3pm has 59358 (40.8%) missing values Missing
Temp9am has 1767 (1.2%) missing values Missing
Temp3pm has 3609 (2.5%) missing values Missing
RainToday has 3261 (2.2%) missing values Missing
RainTomorrow has 3267 (2.2%) missing values Missing
Rainfall has 91080 (62.6%) zeros Zeros
Sunshine has 2359 (1.6%) zeros Zeros
WindSpeed9am has 8745 (6.0%) zeros Zeros
Cloud9am has 8642 (5.9%) zeros Zeros
Cloud3pm has 4974 (3.4%) zeros Zeros

Reproduction

Analysis started2021-12-18 10:10:48.218808
Analysis finished2021-12-18 10:11:38.783803
Duration50.56 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY

Distinct3436
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2013-11-12
 
49
2014-09-01
 
49
2014-08-23
 
49
2014-08-24
 
49
2014-08-25
 
49
Other values (3431)
145215 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)0.1%

Sample

1st row2008-12-01
2nd row2008-12-02
3rd row2008-12-03
4th row2008-12-04
5th row2008-12-05

Common Values

ValueCountFrequency (%)
2013-11-1249
 
< 0.1%
2014-09-0149
 
< 0.1%
2014-08-2349
 
< 0.1%
2014-08-2449
 
< 0.1%
2014-08-2549
 
< 0.1%
2014-08-2649
 
< 0.1%
2014-08-2749
 
< 0.1%
2014-08-2849
 
< 0.1%
2014-08-2949
 
< 0.1%
2014-08-3049
 
< 0.1%
Other values (3426)144970
99.7%

Length

2021-12-18T14:11:38.870751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-11-1249
 
< 0.1%
2016-12-1349
 
< 0.1%
2017-01-0349
 
< 0.1%
2017-01-0249
 
< 0.1%
2017-01-0149
 
< 0.1%
2016-12-3149
 
< 0.1%
2016-12-3049
 
< 0.1%
2016-12-2949
 
< 0.1%
2016-12-2849
 
< 0.1%
2016-12-2749
 
< 0.1%
Other values (3426)144970
99.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Location
Categorical

HIGH CORRELATION

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Canberra
 
3436
Sydney
 
3344
Darwin
 
3193
Melbourne
 
3193
Brisbane
 
3193
Other values (44)
129101 

Length

Max length16
Median length8
Mean length8.711625189
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbury
2nd rowAlbury
3rd rowAlbury
4th rowAlbury
5th rowAlbury

Common Values

ValueCountFrequency (%)
Canberra3436
 
2.4%
Sydney3344
 
2.3%
Darwin3193
 
2.2%
Melbourne3193
 
2.2%
Brisbane3193
 
2.2%
Adelaide3193
 
2.2%
Perth3193
 
2.2%
Hobart3193
 
2.2%
Albany3040
 
2.1%
MountGambier3040
 
2.1%
Other values (39)113442
78.0%

Length

2021-12-18T14:11:38.963245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canberra3436
 
2.4%
sydney3344
 
2.3%
darwin3193
 
2.2%
melbourne3193
 
2.2%
brisbane3193
 
2.2%
adelaide3193
 
2.2%
perth3193
 
2.2%
hobart3193
 
2.2%
launceston3040
 
2.1%
wollongong3040
 
2.1%
Other values (39)113442
78.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MinTemp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct389
Distinct (%)0.3%
Missing1485
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean12.19403438
Minimum-8.5
Maximum33.9
Zeros159
Zeros (%)0.1%
Negative3464
Negative (%)2.4%
Memory size1.1 MiB
2021-12-18T14:11:39.177288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile1.8
Q17.6
median12
Q316.9
95-th percentile23
Maximum33.9
Range42.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.398494976
Coefficient of variation (CV)0.5247233832
Kurtosis-0.483972117
Mean12.19403438
Median Absolute Deviation (MAD)4.6
Skewness0.02118828401
Sum1755636.1
Variance40.94073795
MonotonicityNot monotonic
2021-12-18T14:11:39.295103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11899
 
0.6%
10.2898
 
0.6%
9.6896
 
0.6%
10.5884
 
0.6%
10.8872
 
0.6%
9872
 
0.6%
10871
 
0.6%
12866
 
0.6%
8.9861
 
0.6%
10.4860
 
0.6%
Other values (379)135196
92.9%
(Missing)1485
 
1.0%
ValueCountFrequency (%)
-8.51
 
< 0.1%
-8.22
 
< 0.1%
-82
 
< 0.1%
-7.81
 
< 0.1%
-7.62
 
< 0.1%
-7.52
 
< 0.1%
-7.31
 
< 0.1%
-7.21
 
< 0.1%
-7.11
 
< 0.1%
-77
< 0.1%
ValueCountFrequency (%)
33.91
 
< 0.1%
31.91
 
< 0.1%
31.81
 
< 0.1%
31.43
< 0.1%
31.21
 
< 0.1%
311
 
< 0.1%
30.72
< 0.1%
30.51
 
< 0.1%
30.31
 
< 0.1%
30.21
 
< 0.1%

MaxTemp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct505
Distinct (%)0.4%
Missing1261
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean23.22134828
Minimum-4.8
Maximum48.1
Zeros14
Zeros (%)< 0.1%
Negative113
Negative (%)0.1%
Memory size1.1 MiB
2021-12-18T14:11:39.413973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.8
5-th percentile12.8
Q117.9
median22.6
Q328.2
95-th percentile35.5
Maximum48.1
Range52.9
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation7.119048846
Coefficient of variation (CV)0.3065734496
Kurtosis-0.2246297848
Mean23.22134828
Median Absolute Deviation (MAD)5.1
Skewness0.2208393481
Sum3348495.2
Variance50.68085647
MonotonicityNot monotonic
2021-12-18T14:11:39.528119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20885
 
0.6%
19843
 
0.6%
19.8840
 
0.6%
20.4834
 
0.6%
19.9823
 
0.6%
20.8817
 
0.6%
19.5812
 
0.6%
18.5811
 
0.6%
21810
 
0.6%
18.2804
 
0.6%
Other values (495)135920
93.4%
(Missing)1261
 
0.9%
ValueCountFrequency (%)
-4.81
< 0.1%
-4.11
< 0.1%
-3.81
< 0.1%
-3.71
< 0.1%
-3.21
< 0.1%
-3.12
< 0.1%
-31
< 0.1%
-2.91
< 0.1%
-2.71
< 0.1%
-2.52
< 0.1%
ValueCountFrequency (%)
48.11
 
< 0.1%
47.32
< 0.1%
471
 
< 0.1%
46.91
 
< 0.1%
46.83
< 0.1%
46.72
< 0.1%
46.61
 
< 0.1%
46.51
 
< 0.1%
46.44
< 0.1%
46.32
< 0.1%

Rainfall
Real number (ℝ≥0)

MISSING
ZEROS

Distinct681
Distinct (%)0.5%
Missing3261
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.36091815
Minimum0
Maximum371
Zeros91080
Zeros (%)62.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:39.642458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile13
Maximum371
Range371
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.478059738
Coefficient of variation (CV)3.591001127
Kurtosis178.1520788
Mean2.36091815
Median Absolute Deviation (MAD)0
Skewness9.83622525
Sum335720.2
Variance71.87749692
MonotonicityNot monotonic
2021-12-18T14:11:39.754686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
091080
62.6%
0.28761
 
6.0%
0.43782
 
2.6%
0.62592
 
1.8%
0.82056
 
1.4%
11759
 
1.2%
1.21535
 
1.1%
1.41377
 
0.9%
1.61200
 
0.8%
1.81104
 
0.8%
Other values (671)26953
 
18.5%
(Missing)3261
 
2.2%
ValueCountFrequency (%)
091080
62.6%
0.1157
 
0.1%
0.28761
 
6.0%
0.365
 
< 0.1%
0.43782
 
2.6%
0.539
 
< 0.1%
0.62592
 
1.8%
0.713
 
< 0.1%
0.82056
 
1.4%
0.915
 
< 0.1%
ValueCountFrequency (%)
3711
< 0.1%
367.61
< 0.1%
278.41
< 0.1%
268.61
< 0.1%
247.21
< 0.1%
2401
< 0.1%
236.81
< 0.1%
2251
< 0.1%
219.61
< 0.1%
216.31
< 0.1%

Evaporation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct358
Distinct (%)0.4%
Missing62790
Missing (%)43.2%
Infinite0
Infinite (%)0.0%
Mean5.468231523
Minimum0
Maximum145
Zeros244
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:39.913510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.6
median4.8
Q37.4
95-th percentile12
Maximum145
Range145
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.193704094
Coefficient of variation (CV)0.7669214583
Kurtosis45.0432665
Mean5.468231523
Median Absolute Deviation (MAD)2.4
Skewness3.761286011
Sum452058.7
Variance17.58715403
MonotonicityNot monotonic
2021-12-18T14:11:40.072974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43339
 
2.3%
82609
 
1.8%
2.22095
 
1.4%
22032
 
1.4%
2.62003
 
1.4%
2.42003
 
1.4%
1.81979
 
1.4%
31973
 
1.4%
3.41967
 
1.4%
3.21956
 
1.3%
Other values (348)60714
41.7%
(Missing)62790
43.2%
ValueCountFrequency (%)
0244
 
0.2%
0.18
 
< 0.1%
0.2503
 
0.3%
0.310
 
< 0.1%
0.4769
0.5%
0.514
 
< 0.1%
0.61097
0.8%
0.724
 
< 0.1%
0.81384
1.0%
0.928
 
< 0.1%
ValueCountFrequency (%)
1451
< 0.1%
86.21
< 0.1%
82.41
< 0.1%
81.21
< 0.1%
77.31
< 0.1%
74.81
< 0.1%
72.21
< 0.1%
70.41
< 0.1%
701
< 0.1%
68.82
< 0.1%

Sunshine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct145
Distinct (%)0.2%
Missing69835
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean7.611177521
Minimum0
Maximum14.5
Zeros2359
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:40.192803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q14.8
median8.4
Q310.6
95-th percentile12.8
Maximum14.5
Range14.5
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation3.785482965
Coefficient of variation (CV)0.4973583857
Kurtosis-0.8294593402
Mean7.611177521
Median Absolute Deviation (MAD)2.6
Skewness-0.4964800381
Sum575595.3
Variance14.32988128
MonotonicityNot monotonic
2021-12-18T14:11:40.316447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02359
 
1.6%
10.71101
 
0.8%
111094
 
0.8%
10.81069
 
0.7%
10.51027
 
0.7%
10.91021
 
0.7%
10.31010
 
0.7%
10.2993
 
0.7%
10984
 
0.7%
11.1978
 
0.7%
Other values (135)63989
44.0%
(Missing)69835
48.0%
ValueCountFrequency (%)
02359
1.6%
0.1542
 
0.4%
0.2521
 
0.4%
0.3433
 
0.3%
0.4326
 
0.2%
0.5322
 
0.2%
0.6298
 
0.2%
0.7344
 
0.2%
0.8320
 
0.2%
0.9323
 
0.2%
ValueCountFrequency (%)
14.51
 
< 0.1%
14.34
 
< 0.1%
14.22
 
< 0.1%
14.16
 
< 0.1%
1415
 
< 0.1%
13.922
 
< 0.1%
13.860
 
< 0.1%
13.7118
0.1%
13.6181
0.1%
13.5183
0.1%

WindGustDir
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing10326
Missing (%)7.1%
Memory size1.1 MiB
W
9915 
SE
9418 
N
9313 
SSE
9216 
E
9181 
Other values (11)
88091 

Length

Max length3
Median length2
Mean length2.194917637
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowWNW
3rd rowWSW
4th rowNE
5th rowW

Common Values

ValueCountFrequency (%)
W9915
 
6.8%
SE9418
 
6.5%
N9313
 
6.4%
SSE9216
 
6.3%
E9181
 
6.3%
S9168
 
6.3%
WSW9069
 
6.2%
SW8967
 
6.2%
SSW8736
 
6.0%
WNW8252
 
5.7%
Other values (6)43899
30.2%
(Missing)10326
 
7.1%

Length

2021-12-18T14:11:40.430934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w9915
 
7.3%
se9418
 
7.0%
n9313
 
6.9%
sse9216
 
6.8%
e9181
 
6.8%
s9168
 
6.8%
wsw9069
 
6.7%
sw8967
 
6.6%
ssw8736
 
6.5%
wnw8252
 
6.1%
Other values (6)43899
32.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WindGustSpeed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct67
Distinct (%)< 0.1%
Missing10263
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean40.03523007
Minimum6
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:40.536039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q131
median39
Q348
95-th percentile65
Maximum135
Range129
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.60706227
Coefficient of variation (CV)0.3398772092
Kurtosis1.41864232
Mean40.03523007
Median Absolute Deviation (MAD)9
Skewness0.874878878
Sum5412643
Variance185.1521435
MonotonicityNot monotonic
2021-12-18T14:11:40.657802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
359215
 
6.3%
398794
 
6.0%
318428
 
5.8%
378047
 
5.5%
337933
 
5.5%
417369
 
5.1%
307038
 
4.8%
436609
 
4.5%
286478
 
4.5%
445432
 
3.7%
Other values (57)59854
41.1%
(Missing)10263
 
7.1%
ValueCountFrequency (%)
61
 
< 0.1%
719
 
< 0.1%
991
 
0.1%
11192
 
0.1%
13532
 
0.4%
15835
 
0.6%
171387
1.0%
191751
1.2%
202627
1.8%
222810
1.9%
ValueCountFrequency (%)
1353
 
< 0.1%
1301
 
< 0.1%
1262
 
< 0.1%
1242
 
< 0.1%
1223
 
< 0.1%
1204
< 0.1%
1174
< 0.1%
1155
< 0.1%
1138
< 0.1%
1113
 
< 0.1%

WindDir9am
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing10566
Missing (%)7.3%
Memory size1.1 MiB
N
11758 
SE
9287 
E
9176 
SSE
9112 
NW
8749 
Other values (11)
86812 

Length

Max length3
Median length2
Mean length2.182810207
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowNNW
3rd rowW
4th rowSE
5th rowENE

Common Values

ValueCountFrequency (%)
N11758
 
8.1%
SE9287
 
6.4%
E9176
 
6.3%
SSE9112
 
6.3%
NW8749
 
6.0%
S8659
 
6.0%
W8459
 
5.8%
SW8423
 
5.8%
NNE8129
 
5.6%
NNW7980
 
5.5%
Other values (6)45162
31.0%
(Missing)10566
 
7.3%

Length

2021-12-18T14:11:40.771272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n11758
 
8.7%
se9287
 
6.9%
e9176
 
6.8%
sse9112
 
6.8%
nw8749
 
6.5%
s8659
 
6.4%
w8459
 
6.3%
sw8423
 
6.2%
nne8129
 
6.0%
nnw7980
 
5.9%
Other values (6)45162
33.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WindDir3pm
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing4228
Missing (%)2.9%
Memory size1.1 MiB
SE
10838 
W
10110 
S
9926 
WSW
9518 
SSE
9399 
Other values (11)
91441 

Length

Max length3
Median length2
Mean length2.207962785
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWNW
2nd rowWSW
3rd rowWSW
4th rowE
5th rowNW

Common Values

ValueCountFrequency (%)
SE10838
 
7.5%
W10110
 
7.0%
S9926
 
6.8%
WSW9518
 
6.5%
SSE9399
 
6.5%
SW9354
 
6.4%
N8890
 
6.1%
WNW8874
 
6.1%
NW8610
 
5.9%
ESE8505
 
5.8%
Other values (6)47208
32.5%

Length

2021-12-18T14:11:40.879553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se10838
 
7.7%
w10110
 
7.2%
s9926
 
7.0%
wsw9518
 
6.7%
sse9399
 
6.7%
sw9354
 
6.6%
n8890
 
6.3%
wnw8874
 
6.3%
nw8610
 
6.1%
ese8505
 
6.0%
Other values (6)47208
33.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WindSpeed9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct43
Distinct (%)< 0.1%
Missing1767
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean14.04342591
Minimum0
Maximum130
Zeros8745
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:41.085681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum130
Range130
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.915375323
Coefficient of variation (CV)0.6348433336
Kurtosis1.226990907
Mean14.04342591
Median Absolute Deviation (MAD)6
Skewness0.7776295123
Sum2017942
Variance79.48391714
MonotonicityNot monotonic
2021-12-18T14:11:41.197223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
913649
 
9.4%
1313132
 
9.0%
1111728
 
8.1%
1710788
 
7.4%
710783
 
7.4%
1510625
 
7.3%
69118
 
6.3%
198763
 
6.0%
08745
 
6.0%
208063
 
5.5%
Other values (33)38299
26.3%
ValueCountFrequency (%)
08745
6.0%
24609
 
3.2%
46360
4.4%
69118
6.3%
710783
7.4%
913649
9.4%
1111728
8.1%
1313132
9.0%
1510625
7.3%
1710788
7.4%
ValueCountFrequency (%)
1301
 
< 0.1%
872
 
< 0.1%
831
 
< 0.1%
744
 
< 0.1%
721
 
< 0.1%
692
 
< 0.1%
674
 
< 0.1%
658
< 0.1%
639
< 0.1%
6111
< 0.1%

WindSpeed3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct44
Distinct (%)< 0.1%
Missing3062
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean18.66265678
Minimum0
Maximum87
Zeros1112
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:41.317575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median19
Q324
95-th percentile35
Maximum87
Range87
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.809800021
Coefficient of variation (CV)0.4720549773
Kurtosis0.7638582384
Mean18.66265678
Median Absolute Deviation (MAD)6
Skewness0.6282154194
Sum2657525
Variance77.61257641
MonotonicityNot monotonic
2021-12-18T14:11:41.422652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1312580
 
8.6%
1712539
 
8.6%
2011713
 
8.1%
1511483
 
7.9%
1911263
 
7.7%
1110015
 
6.9%
99753
 
6.7%
249052
 
6.2%
228598
 
5.9%
286553
 
4.5%
Other values (34)38849
26.7%
ValueCountFrequency (%)
01112
 
0.8%
21034
 
0.7%
42249
 
1.5%
63805
 
2.6%
75903
4.1%
99753
6.7%
1110015
6.9%
1312580
8.6%
1511483
7.9%
1712539
8.6%
ValueCountFrequency (%)
871
 
< 0.1%
832
 
< 0.1%
781
 
< 0.1%
762
 
< 0.1%
741
 
< 0.1%
722
 
< 0.1%
693
 
< 0.1%
671
 
< 0.1%
6518
< 0.1%
6313
< 0.1%

Humidity9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct101
Distinct (%)0.1%
Missing2654
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean68.88083134
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:41.537924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q157
median70
Q383
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.02916445
Coefficient of variation (CV)0.2762621194
Kurtosis-0.03755504182
Mean68.88083134
Median Absolute Deviation (MAD)13
Skewness-0.4839689946
Sum9836596
Variance362.1090997
MonotonicityNot monotonic
2021-12-18T14:11:41.656695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
993391
 
2.3%
703026
 
2.1%
693023
 
2.1%
653014
 
2.1%
683011
 
2.1%
712976
 
2.0%
662973
 
2.0%
672950
 
2.0%
742917
 
2.0%
722914
 
2.0%
Other values (91)112611
77.4%
ValueCountFrequency (%)
01
 
< 0.1%
15
 
< 0.1%
28
 
< 0.1%
310
 
< 0.1%
420
 
< 0.1%
527
 
< 0.1%
637
< 0.1%
743
< 0.1%
856
< 0.1%
971
< 0.1%
ValueCountFrequency (%)
1002863
2.0%
993391
2.3%
982099
1.4%
971789
1.2%
961609
1.1%
951636
1.1%
941764
1.2%
931862
1.3%
921755
1.2%
911869
1.3%

Humidity3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct101
Distinct (%)0.1%
Missing4507
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean51.53911588
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:41.812759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median52
Q366
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.79590166
Coefficient of variation (CV)0.4034974466
Kurtosis-0.5113632484
Mean51.53911588
Median Absolute Deviation (MAD)14
Skewness0.03361436764
Sum7264593
Variance432.4695257
MonotonicityNot monotonic
2021-12-18T14:11:41.974610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
522751
 
1.9%
552738
 
1.9%
572728
 
1.9%
532697
 
1.9%
592690
 
1.8%
582643
 
1.8%
542642
 
1.8%
502624
 
1.8%
512621
 
1.8%
602615
 
1.8%
Other values (91)114204
78.5%
(Missing)4507
 
3.1%
ValueCountFrequency (%)
04
 
< 0.1%
126
 
< 0.1%
235
 
< 0.1%
363
 
< 0.1%
4113
 
0.1%
5157
 
0.1%
6242
0.2%
7303
0.2%
8422
0.3%
9481
0.3%
ValueCountFrequency (%)
100400
0.3%
99434
0.3%
98603
0.4%
97403
0.3%
96462
0.3%
95465
0.3%
94559
0.4%
93607
0.4%
92648
0.4%
91617
0.4%

Pressure9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct546
Distinct (%)0.4%
Missing15065
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean1017.64994
Minimum980.5
Maximum1041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:42.136315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum980.5
5-th percentile1006.2
Q11012.9
median1017.6
Q31022.4
95-th percentile1029.5
Maximum1041
Range60.5
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.106530288
Coefficient of variation (CV)0.006983275888
Kurtosis0.2315626216
Mean1017.64994
Median Absolute Deviation (MAD)4.7
Skewness-0.09552363669
Sum132696463.9
Variance50.50277273
MonotonicityNot monotonic
2021-12-18T14:11:42.300129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016.4816
 
0.6%
1017.9789
 
0.5%
1016.3775
 
0.5%
1018.7775
 
0.5%
1018769
 
0.5%
1017.3769
 
0.5%
1015.9768
 
0.5%
1017.8766
 
0.5%
1017.2759
 
0.5%
1017.7759
 
0.5%
Other values (536)122650
84.3%
(Missing)15065
 
10.4%
ValueCountFrequency (%)
980.51
< 0.1%
9821
< 0.1%
982.21
< 0.1%
982.31
< 0.1%
982.92
< 0.1%
983.71
< 0.1%
983.91
< 0.1%
984.41
< 0.1%
984.62
< 0.1%
9851
< 0.1%
ValueCountFrequency (%)
10411
 
< 0.1%
1040.91
 
< 0.1%
1040.62
< 0.1%
1040.51
 
< 0.1%
1040.43
< 0.1%
1040.33
< 0.1%
1040.22
< 0.1%
1040.13
< 0.1%
10401
 
< 0.1%
1039.93
< 0.1%

Pressure3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct549
Distinct (%)0.4%
Missing15028
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean1015.255889
Minimum977.1
Maximum1039.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:42.463604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum977.1
5-th percentile1004
Q11010.4
median1015.2
Q31020
95-th percentile1026.9
Maximum1039.6
Range62.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.037413808
Coefficient of variation (CV)0.006931665096
Kurtosis0.1291715572
Mean1015.255889
Median Absolute Deviation (MAD)4.8
Skewness-0.0456214048
Sum132421856.1
Variance49.52519311
MonotonicityNot monotonic
2021-12-18T14:11:42.584151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.3786
 
0.5%
1015.5783
 
0.5%
1015.6776
 
0.5%
1015.7773
 
0.5%
1013.5767
 
0.5%
1015.1766
 
0.5%
1015.8765
 
0.5%
1015.4756
 
0.5%
1016747
 
0.5%
1014.8745
 
0.5%
Other values (539)122768
84.4%
(Missing)15028
 
10.3%
ValueCountFrequency (%)
977.11
< 0.1%
978.21
< 0.1%
9791
< 0.1%
980.22
< 0.1%
981.21
< 0.1%
981.41
< 0.1%
981.91
< 0.1%
982.21
< 0.1%
982.61
< 0.1%
982.91
< 0.1%
ValueCountFrequency (%)
1039.61
 
< 0.1%
1038.91
 
< 0.1%
1038.51
 
< 0.1%
1038.41
 
< 0.1%
1038.21
 
< 0.1%
10381
 
< 0.1%
1037.92
< 0.1%
1037.82
< 0.1%
1037.73
< 0.1%
1037.61
 
< 0.1%

Cloud9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing55888
Missing (%)38.4%
Infinite0
Infinite (%)0.0%
Mean4.44746126
Minimum0
Maximum9
Zeros8642
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:42.720435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.887158854
Coefficient of variation (CV)0.6491700961
Kurtosis-1.538830489
Mean4.44746126
Median Absolute Deviation (MAD)3
Skewness-0.2290818322
Sum398368
Variance8.335686245
MonotonicityNot monotonic
2021-12-18T14:11:42.804860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
719972
 
13.7%
115687
 
10.8%
814697
 
10.1%
08642
 
5.9%
68171
 
5.6%
26500
 
4.5%
35914
 
4.1%
55567
 
3.8%
44420
 
3.0%
92
 
< 0.1%
(Missing)55888
38.4%
ValueCountFrequency (%)
08642
5.9%
115687
10.8%
26500
 
4.5%
35914
 
4.1%
44420
 
3.0%
55567
 
3.8%
68171
5.6%
719972
13.7%
814697
10.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
814697
10.1%
719972
13.7%
68171
5.6%
55567
 
3.8%
44420
 
3.0%
35914
 
4.1%
26500
 
4.5%
115687
10.8%
08642
5.9%

Cloud3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing59358
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean4.509930083
Minimum0
Maximum9
Zeros4974
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-18T14:11:42.885688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.72035731
Coefficient of variation (CV)0.6031927902
Kurtosis-1.456524516
Mean4.509930083
Median Absolute Deviation (MAD)2
Skewness-0.2263843461
Sum388314
Variance7.400343896
MonotonicityNot monotonic
2021-12-18T14:11:42.996387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
718229
 
12.5%
114976
 
10.3%
812660
 
8.7%
68978
 
6.2%
27226
 
5.0%
36921
 
4.8%
56815
 
4.7%
45322
 
3.7%
04974
 
3.4%
91
 
< 0.1%
(Missing)59358
40.8%
ValueCountFrequency (%)
04974
 
3.4%
114976
10.3%
27226
 
5.0%
36921
 
4.8%
45322
 
3.7%
56815
 
4.7%
68978
6.2%
718229
12.5%
812660
8.7%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
812660
8.7%
718229
12.5%
68978
6.2%
56815
 
4.7%
45322
 
3.7%
36921
 
4.8%
27226
 
5.0%
114976
10.3%
04974
 
3.4%

Temp9am
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct441
Distinct (%)0.3%
Missing1767
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean16.99063142
Minimum-7.2
Maximum40.2
Zeros36
Zeros (%)< 0.1%
Negative443
Negative (%)0.3%
Memory size1.1 MiB
2021-12-18T14:11:43.251266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile6.9
Q112.3
median16.7
Q321.6
95-th percentile28.2
Maximum40.2
Range47.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.488753141
Coefficient of variation (CV)0.3819018247
Kurtosis-0.3405233369
Mean16.99063142
Median Absolute Deviation (MAD)4.6
Skewness0.0885399966
Sum2441434.8
Variance42.10391732
MonotonicityNot monotonic
2021-12-18T14:11:43.373955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17912
 
0.6%
13.8900
 
0.6%
14.8894
 
0.6%
16882
 
0.6%
14876
 
0.6%
15867
 
0.6%
16.6867
 
0.6%
16.5856
 
0.6%
13848
 
0.6%
15.1846
 
0.6%
Other values (431)134945
92.8%
(Missing)1767
 
1.2%
ValueCountFrequency (%)
-7.21
 
< 0.1%
-71
 
< 0.1%
-6.21
 
< 0.1%
-5.91
 
< 0.1%
-5.62
 
< 0.1%
-5.52
 
< 0.1%
-5.32
 
< 0.1%
-5.25
< 0.1%
-4.91
 
< 0.1%
-4.82
 
< 0.1%
ValueCountFrequency (%)
40.21
< 0.1%
39.41
< 0.1%
39.11
< 0.1%
391
< 0.1%
38.91
< 0.1%
38.61
< 0.1%
38.31
< 0.1%
38.21
< 0.1%
381
< 0.1%
37.91
< 0.1%

Temp3pm
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct502
Distinct (%)0.4%
Missing3609
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean21.68339032
Minimum-5.4
Maximum46.7
Zeros17
Zeros (%)< 0.1%
Negative180
Negative (%)0.1%
Memory size1.1 MiB
2021-12-18T14:11:43.546118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-5.4
5-th percentile11.6
Q116.6
median21.1
Q326.4
95-th percentile33.7
Maximum46.7
Range52.1
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.93665046
Coefficient of variation (CV)0.3199061751
Kurtosis-0.1362814705
Mean21.68339032
Median Absolute Deviation (MAD)4.8
Skewness0.237960364
Sum3075810.6
Variance48.1171196
MonotonicityNot monotonic
2021-12-18T14:11:43.711162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20882
 
0.6%
19869
 
0.6%
18.5869
 
0.6%
18.4868
 
0.6%
17.8859
 
0.6%
19.4840
 
0.6%
18839
 
0.6%
19.2839
 
0.6%
17834
 
0.6%
19.3833
 
0.6%
Other values (492)133319
91.7%
(Missing)3609
 
2.5%
ValueCountFrequency (%)
-5.41
 
< 0.1%
-5.11
 
< 0.1%
-4.41
 
< 0.1%
-4.21
 
< 0.1%
-4.11
 
< 0.1%
-41
 
< 0.1%
-3.92
< 0.1%
-3.81
 
< 0.1%
-3.73
< 0.1%
-3.53
< 0.1%
ValueCountFrequency (%)
46.71
 
< 0.1%
46.21
 
< 0.1%
46.13
< 0.1%
45.91
 
< 0.1%
45.82
< 0.1%
45.41
 
< 0.1%
45.32
< 0.1%
45.22
< 0.1%
451
 
< 0.1%
44.91
 
< 0.1%

RainToday
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing3261
Missing (%)2.2%
Memory size284.2 KiB
False
110319 
True
31880 
(Missing)
 
3261
ValueCountFrequency (%)
False110319
75.8%
True31880
 
21.9%
(Missing)3261
 
2.2%
2021-12-18T14:11:43.829002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

RainTomorrow
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing3267
Missing (%)2.2%
Memory size284.2 KiB
False
110316 
True
31877 
(Missing)
 
3267
ValueCountFrequency (%)
False110316
75.8%
True31877
 
21.9%
(Missing)3267
 
2.2%
2021-12-18T14:11:43.884097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interactions

2021-12-18T14:11:34.211149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:04.171209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:06.240198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:08.319153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:10.307636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:12.275154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:14.220915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:16.226171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:18.269185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:20.316221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:22.290958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:24.376233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:26.470182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:28.456954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:30.356588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:32.285763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:34.334995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:04.316906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:06.367617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:08.437613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:10.416523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:12.381458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:14.343191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:16.348158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:18.387791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:20.439055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:22.422380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:24.503597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:26.591693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:28.581391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:30.470556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:32.399182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:34.454058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:04.533605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:06.486385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:08.560137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:10.531435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:12.493405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:14.466783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:16.576148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:18.507742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:20.565910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:22.544908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:24.622735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:26.708989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:28.689571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:30.573524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:32.518088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:34.566968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:04.657049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:06.608736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:08.680325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:10.749710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:12.609246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:14.594811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:16.695655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:18.623576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:20.683597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:22.660919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:24.756353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:26.842329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:28.815608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:30.685079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:32.630893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:34.680001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:04.767266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:06.728203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:08.791884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:10.864976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:12.745249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:14.717247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:16.802779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:18.749090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:20.808732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:22.899197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:24.873948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:26.958676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:29.029912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:30.800422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:32.767694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:34.808610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:04.897264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:06.854167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:08.917015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:10.982775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:12.860840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:14.861509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:16.934589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:18.879628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:20.933360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:23.033137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:25.007289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:27.093936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:29.140031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:30.915792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:32.889893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:35.051640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:05.024465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:06.981984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:09.057005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:11.095832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:12.978866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:14.992847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:17.059540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:19.005474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:21.070843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:23.159958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:25.133419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:27.224531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:29.259632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:31.029727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:33.024283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:35.174543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:05.155051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:07.107103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:09.197911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:11.216126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:13.094729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:15.111976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:17.189379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:19.122821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:21.193248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:23.293774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:25.267923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:27.355032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:29.370960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:31.141518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:33.143660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:35.292080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:05.273238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:07.233900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:09.326658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:11.343011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:13.228072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:15.240719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:17.302777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:19.246679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:21.312808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:23.409247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:25.384822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:27.471063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:29.474808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:31.248974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:33.262343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:35.412517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:05.394988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:07.363678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:09.455116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:11.453557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:13.336695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:15.365273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:17.427751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:19.373623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:21.431105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:23.533059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:25.521055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:27.603999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:29.584311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:31.364293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:33.379526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:35.527881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:05.511575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:07.484114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:09.576611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:11.568216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:13.547448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:15.485371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:17.544803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:19.489706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:21.561348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:23.651157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:25.647866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:27.729389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:29.689960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:31.476535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:33.506751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:35.649931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:05.653018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:07.738656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:09.710735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:11.692049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:13.664263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:15.614328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:17.680552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:19.720389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:21.687133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:23.787023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:25.786150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:27.863623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:29.818888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:31.593027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:33.627335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:35.761392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:05.767110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:07.852749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:09.832081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:11.815801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:13.777049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:15.740300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:17.794880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:19.834475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:21.813974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:23.902874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:26.003693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:27.980507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:29.932182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:31.702009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:33.752169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:35.884322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:05.881653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:07.956788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:09.942537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:11.921411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:13.884662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:15.850468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:17.905736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:19.940522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:21.920016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:24.011842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:26.109874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:28.091744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:30.038226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:31.941429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:33.862411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:36.001062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:06.003506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:08.076475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:10.066439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:12.033842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:13.991945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:15.970021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:18.023157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:20.068998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:22.040382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:24.127606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:26.231512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:28.210767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:30.143163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:32.044011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:33.979186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:36.117889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:06.122998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:08.193670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:10.196401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:12.160874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:14.101635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:16.091784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:18.143916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:20.191217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:22.156680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:24.262215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:26.351810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:28.346096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:30.254493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:32.148249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-18T14:11:34.093303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2021-12-18T14:11:44.035478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-18T14:11:44.386154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-18T14:11:44.642055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-18T14:11:45.016352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-18T14:11:45.156929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-18T14:11:36.337954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-18T14:11:36.928832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-18T14:11:38.001162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-18T14:11:38.522732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
02008-12-01Albury13.422.90.6NaNNaNW44.0WWNW20.024.071.022.01007.71007.18.0NaN16.921.8NoNo
12008-12-02Albury7.425.10.0NaNNaNWNW44.0NNWWSW4.022.044.025.01010.61007.8NaNNaN17.224.3NoNo
22008-12-03Albury12.925.70.0NaNNaNWSW46.0WWSW19.026.038.030.01007.61008.7NaN2.021.023.2NoNo
32008-12-04Albury9.228.00.0NaNNaNNE24.0SEE11.09.045.016.01017.61012.8NaNNaN18.126.5NoNo
42008-12-05Albury17.532.31.0NaNNaNW41.0ENENW7.020.082.033.01010.81006.07.08.017.829.7NoNo
52008-12-06Albury14.629.70.2NaNNaNWNW56.0WW19.024.055.023.01009.21005.4NaNNaN20.628.9NoNo
62008-12-07Albury14.325.00.0NaNNaNW50.0SWW20.024.049.019.01009.61008.21.0NaN18.124.6NoNo
72008-12-08Albury7.726.70.0NaNNaNW35.0SSEW6.017.048.019.01013.41010.1NaNNaN16.325.5NoNo
82008-12-09Albury9.731.90.0NaNNaNNNW80.0SENW7.028.042.09.01008.91003.6NaNNaN18.330.2NoYes
92008-12-10Albury13.130.11.4NaNNaNW28.0SSSE15.011.058.027.01007.01005.7NaNNaN20.128.2YesNo

Last rows

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
1454502017-06-16Uluru5.224.30.0NaNNaNE24.0SEE11.011.053.024.01023.81020.0NaNNaN12.323.3NoNo
1454512017-06-17Uluru6.423.40.0NaNNaNESE31.0SESE15.017.053.025.01025.81023.0NaNNaN11.223.1NoNo
1454522017-06-18Uluru8.020.70.0NaNNaNESE41.0SEE19.026.056.032.01028.11024.3NaN7.011.620.0NoNo
1454532017-06-19Uluru7.420.60.0NaNNaNE35.0ESEE15.017.063.033.01027.21023.3NaNNaN11.020.3NoNo
1454542017-06-20Uluru3.521.80.0NaNNaNE31.0ESEE15.013.059.027.01024.71021.2NaNNaN9.420.9NoNo
1454552017-06-21Uluru2.823.40.0NaNNaNE31.0SEENE13.011.051.024.01024.61020.3NaNNaN10.122.4NoNo
1454562017-06-22Uluru3.625.30.0NaNNaNNNW22.0SEN13.09.056.021.01023.51019.1NaNNaN10.924.5NoNo
1454572017-06-23Uluru5.426.90.0NaNNaNN37.0SEWNW9.09.053.024.01021.01016.8NaNNaN12.526.1NoNo
1454582017-06-24Uluru7.827.00.0NaNNaNSE28.0SSEN13.07.051.024.01019.41016.53.02.015.126.0NoNo
1454592017-06-25Uluru14.9NaN0.0NaNNaNNaNNaNESEESE17.017.062.036.01020.21017.98.08.015.020.9NoNaN